


import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def 							UpperCamelCase_(					_A :Tuple	)->		Tuple:
			return EnvironmentCommand()
def 							UpperCamelCase_(					_A :str	)->		List[Any]:
			return EnvironmentCommand(args.accelerate_config_file	)







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			@staticmethod
			def 					snake_case__					(				snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							parser.add_parser("env"  )
						download_parser.set_defaults(func=snake_case  )
						download_parser.add_argument(
						    "--accelerate-config_file"   ,    default=snake_case   ,    help="The accelerate config file to use for the default values in the launching script."   ,    )
						download_parser.set_defaults(func=snake_case  )




			def __init__(				self   ,    snake_case   ,    *snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							accelerate_config_file




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"not installed"
						if is_safetensors_available():
									import safetensors

									UpperCamelCase__       =							safetensors.__version__
						elif importlib.util.find_spec("safetensors"  ) is not None:
									import safetensors

									UpperCamelCase__       =							F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

						UpperCamelCase__       =							"not installed"
						UpperCamelCase__       =							UpperCamelCase__       =							"not found"
						if is_accelerate_available():
									import accelerate
									from accelerate.commands.config import default_config_file, load_config_from_file

									UpperCamelCase__       =							accelerate.__version__
									# Get the default from the config file.
									if self._accelerate_config_file is not None or os.path.isfile(snake_case  ):
												UpperCamelCase__       =							load_config_from_file(self._accelerate_config_file  ).to_dict()

									UpperCamelCase__       =							(
									    "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
									    if isinstance(snake_case   ,    snake_case  )
									    else F'''\t{accelerate_config}'''
									)

						UpperCamelCase__       =							"not installed"
						UpperCamelCase__       =							"NA"
						if is_torch_available():
									import torch

									UpperCamelCase__       =							torch.__version__
									UpperCamelCase__       =							torch.cuda.is_available()

						UpperCamelCase__       =							"not installed"
						UpperCamelCase__       =							"NA"
						if is_tf_available():
									import tensorflow as tf

									UpperCamelCase__       =							tf.__version__
									try:
												# deprecated in v2.1
												UpperCamelCase__       =							tf.test.is_gpu_available()
									except AttributeError:
												# returns list of devices, convert to bool
												UpperCamelCase__       =							bool(tf.config.list_physical_devices("GPU"  )  )

						UpperCamelCase__       =							"not installed"
						UpperCamelCase__       =							"not installed"
						UpperCamelCase__       =							"not installed"
						UpperCamelCase__       =							"NA"
						if is_flax_available():
									import flax
									import jax
									import jaxlib

									UpperCamelCase__       =							flax.__version__
									UpperCamelCase__       =							jax.__version__
									UpperCamelCase__       =							jaxlib.__version__
									UpperCamelCase__       =							jax.lib.xla_bridge.get_backend().platform

						UpperCamelCase__       =							{
						    "`transformers` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "Huggingface_hub version": huggingface_hub.__version__,
						    "Safetensors version": F'''{safetensors_version}''',
						    "Accelerate version": F'''{accelerate_version}''',
						    "Accelerate config": F'''{accelerate_config_str}''',
						    "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
						    "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
						    "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
						    "Jax version": F'''{jax_version}''',
						    "JaxLib version": F'''{jaxlib_version}''',
						    "Using GPU in script?": "<fill in>",
						    "Using distributed or parallel set-up in script?": "<fill in>",
						}

						print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"  )
						print(self.format_dict(snake_case  )  )

						return info







			@staticmethod
			def 					snake_case__					(				snake_case  ):

						'''simple docstring'''


						return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]  ) + "\n"






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		MvpTokenizer
			_UpperCamelCase		:       Any 			=		MvpTokenizerFast
			_UpperCamelCase		:       List[str] 			=		True
			_UpperCamelCase		:       Dict 			=		filter_roberta_detectors




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().setUp()
						UpperCamelCase__       =							[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						UpperCamelCase__       =							dict(zip(snake_case   ,    range(len(snake_case  )  )  )  )
						UpperCamelCase__       =							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						UpperCamelCase__       =							{"unk_token": "<unk>"}

						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["vocab_file"]  )
						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["merges_file"]  )
						with open(self.vocab_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write(json.dumps(snake_case  ) + "\n"  )
						with open(self.merges_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write("\n".join(snake_case  )  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return "lower newer", "lower newer"




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizer.from_pretrained("RUCAIBox/mvp"  )




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						UpperCamelCase__       =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    max_length=len(snake_case  )   ,    padding=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )

									self.assertEqual((2, 9)   ,    batch.input_ids.shape  )
									self.assertEqual((2, 9)   ,    batch.attention_mask.shape  )
									UpperCamelCase__       =							batch.input_ids.tolist()[0]
									self.assertListEqual(snake_case   ,    snake_case  )
									# Test that special tokens are reset




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    padding=snake_case   ,    return_tensors="pt"  )
									# check if input_ids are returned and no labels
									self.assertIn("input_ids"   ,    snake_case  )
									self.assertIn("attention_mask"   ,    snake_case  )
									self.assertNotIn("labels"   ,    snake_case  )
									self.assertNotIn("decoder_attention_mask"   ,    snake_case  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							[
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(text_target=snake_case   ,    max_length=32   ,    padding="max_length"   ,    return_tensors="pt"  )
									self.assertEqual(32   ,    targets["input_ids"].shape[1]  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(
									    ["I am a small frog" * 1024, "I am a small frog"]   ,    padding=snake_case   ,    truncation=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )
									self.assertEqual(batch.input_ids.shape   ,    (2, 1024)  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization."]
						UpperCamelCase__       =							[
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    text_target=snake_case   ,    return_tensors="pt"  )
									UpperCamelCase__       =							inputs["input_ids"]
									UpperCamelCase__       =							inputs["labels"]
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
												UpperCamelCase__       =							self.rust_tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							self.tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							"A, <mask> AllenNLP sentence."
												UpperCamelCase__       =							tokenizer_r.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )
												UpperCamelCase__       =							tokenizer_p.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )

												# token_type_ids should put 0 everywhere
												self.assertEqual(sum(tokens_r["token_type_ids"]  )   ,    sum(tokens_p["token_type_ids"]  )  )

												# attention_mask should put 1 everywhere, so sum over length should be 1
												self.assertEqual(
												    sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  )   ,    sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  )   ,    )

												UpperCamelCase__       =							tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
												UpperCamelCase__       =							tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )

												# Rust correctly handles the space before the mask while python doesnt
												self.assertSequenceEqual(tokens_p["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )
												self.assertSequenceEqual(tokens_r["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )

												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )


